SoroushVahidi/scidocs huggingface dataset:
Reduced keyframes for fast bundle adjustment using point and line features in monoslam
(2017)

Visual Simultaneous Localization and Mapping (VSLAM) requires feature detection on visual data. In indoor scenes that include architectures such as plain walls and doors, there are no or less corners detected, in such cases tracking will be lost. To overcome this situation we track different types of features that help in feature extraction even in textureless scenes. Line features are used to get the outline of the structure. This paper explains method of line based localization when no point features are detected and process of mapping the matched lines between two frames. One of the existing state of art monocular SLAM that extracts point features only, is used as the main build and LBD (Line Band Descriptor) is incorporated in it for localization and mapping. Our new system extracts more features compared to the existing system and complexity of the bundle adjustment is decreased as it decreases the keyframes selected for local mapping and optimization when tested using our own dataset and compared with the existing system.
Teaching the Basics of NLP and ML in an Introductory Course to Information Science
(2013)

In this paper we discuss our experience of teaching basic Natural Language Processing (NLP) and Machine Learning (ML) in an introductory course to Information Science. We discuss the challenges we faced while incorporating NLP and ML to the curriculum followed by a presentation of how we met these challenges. The overall response (of students) to the inclusion of this new topic to the curriculum has been positive. Students this semester are pursuing NLP/ML projects, formulating their own tasks (some of which are novel and presented towards the end of the paper), collecting and annotating data and building models for their task.
Echo State Queueing Network: A new reservoir computing learning tool
(2013)

In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due to its success in solving learning tasks and other computational applications. Some success was also observed with another recently proposed neural network designed using Queueing Theory, the Random Neural Network (RandNN). Both approaches have good properties and identified drawbacks. In this paper, we propose a new RC model called Echo State Queueing Network (ESQN), where we use ideas coming from RandNNs for the design of the reservoir. ESQNs consist in ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The paper positions ESQNs in the global Machine Learning area, and provides examples of their use and performances. We show on largely used benchmarks that ESQNs are very accurate tools, and we illustrate how they compare with standard ESNs.
Experiences of stigma and access to HAART in children and adolescents living with HIV/AIDS in Brazil.
(2006)

This study describes and conceptualizes the experiences of stigma in a group of children living with HIV in São Paulo, Brazil, and evaluates the impact of access to highly active antiretroviral therapy (HAART) over the social course of AIDS and over the children's experiences of stigma. Through ethnographic research in São Paulo from 1999 to 2001, the life trajectories of 50 children ages 1-15 living with or affected by HIV were studied. Data were collected via participant observation and semi-structured informal interviews and analyzed using social theories on illness experience and social inequality. Our results demonstrate that AIDS-related stigma occurs within complex discrimination processes that change as children reach adolescence. We found that structural violence in the forms of poverty, racism, and inequalities in social status, gender, and age fuels children's experiences of stigma. We also describe how access to HAART changes the lived experience of children, reduces stigma, and brings new challenges in AIDS care such as adolescents' sexuality and treatment adherence. Based on these results, we propose structural violence as the framework to study stigma and argue that interventions to reduce stigma that solely target the perception and attitudes toward people living with HIV are limited. In contrast universal access to HAART in Brazil is a powerful intervention that reduces stigma, in that it transforms AIDS from a debilitating and fatal disease to a chronic and manageable one, belongs to a broader mechanism to assure citizens' rights, and reduces social inequalities in access to health care.
Bricks, Blocks, Boxes, Cubes, and Dice: On the Role of Cubic Shapes for the Design of Tangible Interactive Devices
(2018)

Cubic shapes play an astonishing role in the design of tangible interactive devices. Due to our curiosity for this widespread design preference lasting over thirty years, we constituted a literature survey of papers, books and products since the late 1970s. Out of a corpus of fourty-seven papers, books and products that propose cubic shapes for tangible interactive devices we trace the origins of cubicle tangibles and highlight the rationale for their application. Through a comparative study, we analyze the properties of this shape for tangible interaction design and classify these along the themes of: Manipulation as Input, Placement in Space as Input, Arrangement, Multifunctionality, Randomness, Togetherness & Variations, Physical Qualities, Container, and Pedestal for Output. We propose a taxonomy for cubic shaped tangible interactive devices based on the reviewed contributions, in order to support researchers and designers in their future work of designing cubic shaped tangible interactive devices.
A case study of a five-year-old child with pervasive developmental disorder-not otherwise specified using sound-based interventions.
(2009)

The aim of this study was to determine the efficacy of The Listening Program (TLP) in treating a child with pervasive developmental disorder-not otherwise specified (PDD-NOS). Using a single-subject case study design, one child with PDD-NOS was administered a 20-week TLP intervention focused on improving sensory processing and language function. Data collection included pre- and post-evaluations using video footage, and Sensory Profile and Listening Checklist questionnaires. Results of the study indicated improved behaviour and sensory tolerance in the post-intervention video footage, including active participation in singing and movements to song. Sensory Profile and Listening Checklist questionnaires indicated significant improvements in sensory processing, receptive/expressive listening and language, motor skills, and behavioural/social adjustment at the post-intervention assessment. Although small in scope, this study highlights the need for continued research by occupational therapists into sound-based interventions. Particularly, occupational therapists need to perform larger-scale studies utilizing TLP to verify the efficacy of this alternative treatment method.
Health, Inequality, and Economic Development
(2003)

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Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction
(2018)

In robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it, and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of graphics processing unit (GPU) computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real time. In this letter, we improved and extended the state-of-the-art incremental manifold mesh algorithm proposed by Litvinov and Lhuillier and extended by Romanoni and Matteucci. While these algorithms do not reconstruct the map in real time and they embed points from SLAM or structure from motion only when their position is fixed, in this letter, we propose the first incremental algorithm able to reconstruct a manifold mesh in real time through single core CPU processing, which is also able to modify the mesh according to three-dimensional points updates from the underlying SLAM algorithm. We tested our algorithm against two state-of-the-art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.
Does the use of social networking sites increase children's risk of harm?
(2013)

Although research findings have been equivocal as to whether the use of social networking sites (SNS) increases experiences of online risk among children, the affordances of SNS lend support to this possibility, attracting much policy and public concern. The present article examines whether the use of such services increases the risks that children and young people encounter by analyzing data from a random stratified sample of approximately 1000 internetusing children aged 9-16 years in each of 25 European countries. Four hypotheses were formulated and tested. The first hypothesis, namely that children who use social networking sites will encounter more risks online than those who do not, is supported by the data. The second hypothesis stated that SNS users with more digital competence will encounter more online risk than those with less competence; this was also supported, despite being counter to common assumptions. Thirdly, we hypothesized that SNS users with more risky SNS practices (e.g. a public profile, displaying identifying information, with a very large number of contacts) will encounter more online risk than those with fewer risky practices: this too was supported by the data; thus what matters for risk is how SNS are used, a useful point for awareness-raising initiatives. The fourth hypothesis stated that SNS users with more digital competence in using the internet will experience less harm associated with online risk. The data did not support this hypothesis, since digital competence did not reduce the probability of children saying that they have been bothered or upset by something on the internet. Finally, the study found that, although this had not been predicted, whether or not risks are experienced as harmful depends on the specific relation between risks and platforms (website, instant messaging, gaming or social networking). We call on future research to explore how particular affordances sustain particular communicative conditions and, in turn, are responded to differently by children. The research and policy implications of the findings are discussed.
Textile Materials for the Design of Wearable Antennas: A Survey
(2012)

In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented.
Computer Game Mods, Modders, Modding, and the Mod Scene
(2010)

Computer games have increasingly been the focus of user-led innovations in the form of game mods. This paper examines how different kinds of socio-technical affordances serve to organize the actions of the people who develop and share their game mods. The affordances examined include customization and tailoring mechanisms, software and content copyright licenses, game software infrastructure and development tools, career contingencies and organizational practices of mod teams, and social worlds intersecting the mod scene. Numerous examples will be used to ground this review and highlight how such affordances can organize, facilitate or constrain what can be done. Overall, this study helps to provide a deeper understanding of how a web of associated affordances collectively serve to govern what mods get made, how modding practices emerge and flourish, and how modders and the game industry serve each others' interests, though not always in equivocal terms.
Feature selection and the class imbalance problem in predicting protein function from sequence.
(2005)

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.
A Category-Based Model for ABAC
(2018)

In Attribute-Based Access Control (ABAC) systems, access to resources is controlled by evaluating rules against the attributes of the user and the object involved in the access request, as well as the values of the relevant attributes from the environment. This is a powerful concept: ABAC is able to enforce DAC and RBAC policies, as well as more general, dynamic access control policies, where the decision to grant or deny an access request is based on the system's state. However, in its current definition, ABAC does not lend itself well to some operations, such as review queries, and it is in general more costly to specify and maintain than simpler systems such as RBAC. To address these issues, in this paper we propose a formal model of ABAC based on the notion of a category that underlies the general category-based metamodel of access control (CBAC). Our proposed approach adds structure to ABAC, so that policies are easier to design and understand, review queries become easy to evaluate, and simple systems such as RBAC can be implemented as instances of ABAC without additional costs.
SAP HANA - From Relational OLAP Database to Big Data Infrastructure
(2015)

SAP HANA started as one of the best-performing database engines for OLAP workloads strictly pursuing a main-memory centric architecture and exploiting hardware developments like large number of cores and main memories in the TByte range. Within this paper, we outline the steps from a traditional relational database engine to a Big Data infrastructure comprising different methods to handle data of different volume, coming in with different velocity, and showing a fairly large degree of variety. In order to make the presentation of this transformation process more tangible, we discuss two major technical topics–HANA native integration points as well as extension points for collaboration with Hadoop-based data management infrastructures. The overall of goal of this paper is to (a) review current application patterns and resulting technical challenges as well as to (b) paint the big picture for upcoming architectural designs with SAP HANA database as the core of a SAP Big Data infrastructure.
Predictive Control of a Three-Level Boost Converter and an NPC Inverter for High-Power PMSG-Based Medium Voltage Wind Energy Conversion Systems
(2014)

In this paper, a new medium voltage power converter topology using a diode rectifier, three-level boost (TLB) converter, and neutral-point-clamped (NPC) inverter is proposed for a high-power permanent magnet synchronous generator-based wind energy conversion system. The generator-side TLB converter performs the maximum power point tracking and balancing of dc-link capacitor voltages, while the grid-side NPC inverter regulates the net dc-bus voltage and reactive power to the grid. A significant improvement in the grid power quality is accomplished as the NPC inverter no longer controls the dc-link neutral point voltage. A model predictive strategy is proposed to control the complete system where the discrete-time models of the proposed power electronic converters are used to predict the future behavior of control variables. These predictions are evaluated using two independent cost functions, and the switching states which minimize these cost functions are selected and applied to the generator- and grid-side converters directly. In order to comply with the high-power application, the switching frequencies of the TLB converter and NPC inverter are minimized and maintained below 1.5 and 1 kHz, respectively. The proposed topology and control strategy are verified through MATLAB simulations on a 3-MW/3000-V/577-A system and dSPACE DS1103-based experiments on 3.6-kW/208-V/10-A prototype.
Maintaining Views Incrementally
(1993)

We present incremental evaluation algorithms to compute changes to materialized views in relational and deductive database systems, in response to changes (insertions, deletions, and updates) to the relations. The view definitions can be in SQL or Datalog, and may use UNION, negation, aggregation (e.g. SUM, MIN), linear recursion, and general recursion.
We first present a counting algorithm that tracks the number of alternative derivations (counts) for each derived tuple in a view. The algorithm works with both set and duplicate semantics. We present the algorithm for nonrecursive views (with negation and aggregation), and show that the count for a tuple can be computed at little or no cost above the cost of deriving the tuple. The algorithm is optimal in that it computes exactly those view tuples that are inserted or deleted. Note that we store only the number of derivations, not the derivations themselves.
We then present the Delete and Rederive algorithm, DRed, for incremental maintenance of recursive views (negation and aggregation are permitted). The algorithm works by first deleting a superset of the tuples that need to be deleted, and then rederiving some of them. The algorithm can also be used when the view definition is itself altered.
Modeling Relation Paths for Representation Learning of Knowledge Bases
(2015)

Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text. The source code of this paper can be obtained from https://github.com/mrlyk423/ relation_extraction.
Transition-based Knowledge Graph Embedding with Relational Mapping Properties
(2014)

Many knowledge repositories nowadays contain billions of triplets, i.e. (head-entity, relationship, tail-entity), as relation instances. These triplets form a directed graph with entities as nodes and relationships as edges. However, this kind of symbolic and discrete storage structure makes it difficult for us to exploit the knowledge to enhance other intelligenceacquired applications (e.g. the QuestionAnswering System), as many AI-related algorithms prefer conducting computation on continuous data. Therefore, a series of emerging approaches have been proposed to facilitate knowledge computing via encoding the knowledge graph into a low-dimensional embedding space. TransE is the latest and most promising approach among them, and can achieve a higher performance with fewer parameters by modeling the relationship as a transitional vector from the head entity to the tail entity. Unfortunately, it is not flexible enough to tackle well with the various mapping properties of triplets, even though its authors spot the harm on performance. In this paper, we thus propose a superior model called TransM to leverage the structure of the knowledge graph via pre-calculating the distinct weight for each training triplet according to its relational mapping property. In this way, the optimal function deals with each triplet depending on its own weight. We carry out extensive experiments to compare TransM with the state-of-the-art method TransE and other prior arts. The performance of each approach is evaluated within two different application scenarios on several benchmark datasets. Results show that the model we proposed significantly outperforms the former ones with lower parameter complexity as TransE.